Simulating a basketball match with a homogeneous Markov model and forecasting the outcome
We used a possession-based Markov model to model the progression of a basketball match. The model’s transition matrix was estimated directly from NBA play-by-play data and indirectly from the teams’ summary statistics. We evaluated both this approach and other commonly used forecasting approaches: logit regression of the outcome, a latent strength rating method, and bookmaker odds. We found that the Markov model approach is appropriate for modelling a basketball match and produces forecasts of a quality comparable to that of other statistical approaches, while giving more insight into basketball. Consistent with previous studies, bookmaker odds were the best probabilistic forecasts.
Volume (Year): 28 (2012)
Issue (Month): 2 ()
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- Zak, Thomas A & Huang, Cliff J & Siegfried, John J, 1979. "Production Efficiency: The Case of Professional Basketball," The Journal of Business, University of Chicago Press, vol. 52(3), pages 379-392, July.
- John M. Gandar & William H. Dare & Craig R. Brown & Richard A. Zuber, 1998. "Informed Traders and Price Variations in the Betting Market for Professional Basketball Games," Journal of Finance, American Finance Association, vol. 53(1), pages 385-401, 02.
- Franck, Egon & Verbeek, Erwin & Nüesch, Stephan, 2010.
"Prediction accuracy of different market structures -- bookmakers versus a betting exchange,"
International Journal of Forecasting,
Elsevier, vol. 26(3), pages 448-459, July.
- Egon Franck & Erwin Verbeek & Stephan Nüesch, 2008. "Prediction Accuracy of Different Market Structures – Bookmakers versus a Betting Exchange," Working Papers 0096, University of Zurich, Institute for Strategy and Business Economics (ISU), revised 2009.
- Egon Franck & Erwin Verbeek & Stephan Nüesch, 2008. "Prediction Accuracy of Different Market Structures – Bookmakers versus a Betting Exchange," Working Papers 0025, University of Zurich, Center for Research in Sports Administration (CRSA), revised 2009.
- Kubatko Justin & Oliver Dean & Pelton Kevin & Rosenbaum Dan T, 2007. "A Starting Point for Analyzing Basketball Statistics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-24, July.
- John M. Gandar & Richard A. Zuber & William H. Dare, 2000. "The Search for Informed Traders in the Totals Betting Market for National Basketball Association Games," Journal of Sports Economics, , vol. 1(2), pages 177-186, May.
- Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
- Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, Research Program on Forecasting.
- Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
- David J. Berri, 1999. "Who is 'most valuable'? Measuring the player's production of wins in the National Basketball Association," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 411-427. Full references (including those not matched with items on IDEAS)
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